class_docstr: Base class for interfacing pmdarima.
class_docstr_tok: ["Base","class","for","interfacing","pmdarima","."]
docstr: Make forecasts.
  
  Parameters
  ----------
  fh : array-like
      The forecasters horizon with the steps ahead to to predict.
      Default is
      one-step ahead forecast, i.e. np.array([1]).
  
  Returns
  -------
  y_pred : pandas.Series
      Returns series of predicted values.
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code: def _predict(self, fh, X):
      """Make forecasts.
  
      Parameters
      ----------
      fh : array-like
          The forecasters horizon with the steps ahead to to predict.
          Default is
          one-step ahead forecast, i.e. np.array([1]).
  
      Returns
      -------
      y_pred : pandas.Series
          Returns series of predicted values.
      """
      fh_abs = fh.to_absolute(self.cutoff).to_pandas()
      fh_abs_int = fh.to_absolute_int(
          fh_abs[0], self.cutoff
      ).to_pandas()
      end_int = fh_abs_int[-1] + 2
      # +2 because + 1 for "end" (python index), +1 for starting to count at 1 in fh
  
      if X is not None:
          X = get_slice(X, start=self.cutoff[0], start_inclusive=False)
          X = X.iloc[:end_int]
  
      # distinguish between in-sample and out-of-sample prediction
      fh_oos = fh.to_out_of_sample(self.cutoff)
      fh_ins = fh.to_in_sample(self.cutoff)
  
      # all values are out-of-sample
      if fh.is_all_out_of_sample(self.cutoff):
          y_pred = self._predict_fixed_cutoff(fh_oos, X=X)
  
      # all values are in-sample
      elif fh.is_all_in_sample(self.cutoff):
          y_pred = self._predict_in_sample(fh_ins, X=X)
  
      # both in-sample and out-of-sample values
      else:
          y_ins = self._predict_in_sample(fh_ins, X=X)
          y_oos = self._predict_fixed_cutoff(fh_oos, X=X)
          y_pred = pd.concat([y_ins, y_oos])
  
      # ensure that name is not added nor removed
      # otherwise this may upset conversion to pd.DataFrame
      y_pred.name = self._y.name
      y_pred.index = fh_abs
      return y_pred
  
file_path: sktime/sktime/forecasting/base/adapters/_pmdarima.py
imports: import pandas
  from sktime.datatypes._utilities import get_slice
  from sktime.forecasting.base import BaseForecaster
  from sktime.forecasting.base._base import DEFAULT_ALPHA
label_desc: Use image node_id 5 for calling the _PmdArimaAdapter obj's underlying member method code with example usage: obj._predict(fh, X) and returns: y_pred